class_docstr: Invertible `TextEncoder` using word pieces with a byte-level fallback.
  
  Encoding is fully invertible because all out-of-vocab wordpieces are
  byte-encoded.
  
  The vocabulary is "trained" on a corpus and all wordpieces are stored in a
  vocabulary file. To generate a vocabulary from a corpus, use
  `tfds.deprecated.text.SubwordTextEncoder.build_from_corpus`.
  
  Typical usage:
  
  ```
  # Build
  encoder = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(
      corpus_generator, target_vocab_size=2**15)
  encoder.save_to_file(vocab_fname)
  
  # Load
  encoder = tfds.deprecated.text.SubwordTextEncoder.load_from_file(vocab_fname)
  ids = encoder.encode("hello world")
  text = encoder.decode([1, 2, 3, 4])
  ```
docstr: Save the vocabulary to a file.
docstr_tok: ["Save","the","vocabulary","to","a","file","."]
code: def save_to_file(self, filename_prefix):
      """Save the vocabulary to a file."""
      # Wrap in single quotes to make it easier to see the full subword when
      # it has spaces and make it easier to search with ctrl+f.
      filename = self._filename(filename_prefix)
      lines = ["'%s'" % s for s in self._subwords]
      self._write_lines_to_file(filename, lines)
  
imports: from __future__ import unicode_literals
  import collections
  from absl import logging
  from tensorflow_datasets.core.deprecated.text import text_encoder
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 12 for calling the SubwordTextEncoder obj's underlying member method code with example usage: obj.save_to_file(filename_prefix) without return types